Some modern video surveillance systems capture tremendous amounts of data for households and businesses, often allowing users to review the footage on their smartphones or tablets. The content ordinarily needs progressively larger storage solutions. However, edge AI vision offers numerous advantages to address that challenge.

Video management

Those with video surveillance storage concerns should consider edge AI vision because it can process data directly on devices. Depending on how electronics engineers or other professionals set up the solutions, they could result in fewer files sent to the cloud or additional offsite repositories.

(Source: Adobe AI Generated)

For example, even some artificial-intelligence security products for home use can differentiate between human-caused movement and other motion. Most homeowners do not want to keep or review footage of garden flags flapping in the wind. However, they would care if cameras caught unusual vehicular or pedestrian traffic in their driveways.

Network Optix is an AI-driven operational security company. It has worked with Intel Corp. to develop edge-processing capabilities that enhance user-friendliness and support prompt decision-making.

One of the company’s executives explained how edge technology solved a customer pain point—slow uploading and search through archived footage, which was particularly a challenge for global companies with cameras collecting content from several locations. Now, users can retrieve a full year of data in seconds and receive relevant alerts.

In addition, Network Optix’s platform uses object-classification algorithms that differentiate between humans, cars, animals, and other objects. Crucially, these options do not perform facial recognition, making them ideal for industrial users who want to review and store footage without sacrificing privacy.

Local storage

Many customers operating video surveillance systems want solutions with locally available storage. Besides allowing them to reduce data center costs, customers can aggregate content even during network outages. It also creates opportunities to install monitoring products in remote locations that have little or no human oversight.

For example, scientists frequently use cameras to study extreme weather. Even if it is unsafe for them to be in those conditions, robust surveillance solutions can continue to monitor and record data even during violent storms that cause power outages and other issues that temporarily disrupt networks.

Local storage gives users the flexibility to store data in multiple ways, especially as newer cameras progressively ingest more data. Some 4K models, for example, generate 7 GB each hour, necessitating powerful, customizable solutions.

Increased privacy

As more executives adopt video surveillance systems, some consumers push back. Most people love convenience, and many will pay for it, but not necessarily at the expense of their privacy.

Robot vacuums are one example. Those who use them love how they eliminate a necessary but boring task. Some people use apps to make the gadgets operate on a schedule, ensuring sparkling-clean floors on demand. The products typically feature on-board AI and other advanced systems to map users’ homes. Such technologies help the bots steer around obstacles and recognize no-go zones.

However, that data goes elsewhere because companies often store it on internal servers. As consumers hear about breaches, identity theft, and private information dumps, many want to limit their exposure. Some robotic vacuums never process content externally, protecting images of residences.

Reduced resources

Video surveillance storage solutions often accompany cameras that record data almost constantly. For example, license-plate readers installed on busy highways or technologies deployed at airports rarely or never have downtime. Taking them offline, even briefly, creates critical gaps in monitoring.

As businesses learn more about the available options for storage solutions, they often discover that the ideal setups are financially prohibitive. That realization forces them to scale back initial plans, with some abandoning their plans entirely due to budgetary concerns.

Qualcomm Technologies Inc. has targeted this common problem with its video-surveillance-as-a-service offering. It uses edge vision to perform detection with analytics on edge AI cameras.

Customers can also request a wholly on-premises operation if desired. That option lowers bandwidth and storage costs to optimize budgets.

Additionally, subscribers can use generative AI and large language models to run queries on the data. For example, a construction site manager could prompt the system to determine how many workers wore safety vests during the previous day.

This option makes the technology accessible while simultaneously managing storage needs and other requirements when custom-built solutions prove too expensive for some executives interested in exploring AI.

Although many businesses believe video surveillance systems can meet numerous operational needs, the estimated storage costs often concern them. Because edge vision minimizes the infrastructure required to store data, by using on-device processing, technologies that use it can create new opportunities.

The post How does edge AI vision meet surveillance storage demands? appeared first on Electronic Products.